A regressive boosting approach to automatic audio tagging based on soft annotator fusion
Rémi Foucard, Slim Essid, Mathieu Lagrange, Gaël Richard · 2012
Automatic tagging of music has mostly been treated as a classification problem. In this framework, the association of a tag to a song is characterized in a “hard” fashion: the tag is either relevant or not. Yet, the relevance of a tag to a song is not always evident. Indeed, during the ground-truth annotation process, several annotators may express doubts, or disagree with each other. In this paper, we propose to fuse annotators' decisions in a way to keep information about this uncertainty. This fusion provides us continuous scores, that are used for training a regressive boosting algorithm. Our experiments show that regression with this soft ground truth leads to a more accurate learning, and better predictions, compared to traditionally used binary classification.